Patient motion is one of the dominant sources of artifact in Magnetic Resonance Imaging (MRI) images. MRI techniques generally involve capturing data measurements (sometimes referred to k-space measurements) for a sequence of slices over a period of time. During the capture stage, movement of a subject may cause a particular slice to become corrupted. While many motion correction techniques for MRI have been proposed, their use is often limited by the need for increased patient preparation, decreased patient comfort, additional scan time, or the use of specialized sequences not available on many commercial scanners. Techniques developed to detect and correct motion include those that utilize specialized hardware to detect patient motion, those that utilize special k-space trajectories with some inherent motion correction ability, those that acquire additional navigator data for the purpose of motion correction, and those considered to be self-navigating.
Motion detection techniques that rely on specialized hardware may include utilizing laser beams which reflect light off of specialized markers. Optical or infrared tracking systems which use multiple cameras or sensors may also be implemented. Other methods may implement tracking using small receiver coils configured to detect motion changes or by using spatial-frequency tuned markers.
Techniques that use specialized k-space trajectories for motion detection may include acquiring rotated sets of overlapping parallel lines or interleaving spiral trajectories. Other methods may include acquiring data in some sort of hybrid Radial-Cartesian fashion or using alternating frequency/phase encode directions.
Some motion detection techniques acquire additional navigator data as a Cartesian projection in the absence of either phase or frequency encoding or with a floating navigator. Orbital navigators utilize a circular k-space trajectory to detect object motion while spherical navigators sample spherical k-space shells. When multiple receive coils are used, parallel imaging techniques such as SENSE or GRAPPA can use a subset of acquired data to predict other measurements that may or may not already be acquired. These predicted k-space lines are compared to the same k-space lines that are actually acquired. Both of these parallel imaging methods require the acquisition of additional calibration data, thus increasing scan time. While all of these motion detection techniques have shown success in their respective applications, their use is often limited by one or more of the following: increased complexity in patient preparation, decreased patient comfort, additional scan time or the required use of specialized sequences not available on many commercial scanners.
Several proposed self-navigating techniques address a specific type of motion (typically in-plane and rigid-body) but none address both rigid and non-rigid body motion that can occur both in and out of plane. For example, motion in the readout direction has been detected by taking the Fourier Transform of a line of acquired data and trying to determine the edges of the object's profile. The edges become increasingly difficult to determine from lines encoded near the edges of k-space and high contrast markers are often added to the patient to overcome this problem. Motion in the phase encode direction can be detected using a symmetric density constraint along the phase encoding axis. However, the algorithm is restrictive on the object type and may not perform well for large motion in the phase encode direction. Another approach is to apply a spatial constraint to the object and then use an iterative phase retrieval algorithm to calculate the desired phase of the object. The calculated phase is compared to the measured phase to simultaneously find motion in the readout and phase encode directions. The algorithm performs well for sub-pixel motions but is unable to correct an artifact caused by large translations. Combinations of these self navigating methods have also been proposed to overcome some of their pitfalls, but have not proven to be robust in the presence of out-of-plane or non-rigid body motion.
Motion may also be determined by iteratively minimizing the entropy of motion-induced ghosts and blurring in an otherwise dark region of an image. Alternatively, data correlations between adjacent data lines can provide information about in-plane rigid body translation. Radial sequences can provide a self-navigating method for rigid-body motion correction using moments of spatial projections or the phase properties of radial trajectories. Motion correction in the slice direction has also been explored by monitoring amplitude modulations of the acquired data. A combination of some of these proposed techniques can be utilized to correct for in-plane rigid-body motion but does not include the ability to address other types of motion.